Introduction: In the current research, it has been tried to qualitatively study all kinds of policies and measures necessary for Alignment between teacher preparation programs and democratic social education competencies under the model of teacher education curricula, which include: direct (official) curriculum, Indirect curriculum, informal curriculum (implicit) and unstructured curriculum (hidden) should be identified in the educational system. Method: Qualitative approach and phenomenological method were used in this research. The field of research is Farhangian University of Hormozgan and the participants are  13  lecturers of this university. The sampling method is a combination of convenience and snowball sampling. The research tool was a semi-structured interview. The information was analyzed using thematic analysis method and then using MAXQDA software... Result: The information was analyzed using the theme analysis method and then using MAXQDA software in the form of 2  overarching themes, 8 organizing themes and 25  basic themes. The results of this research showed the basic policies and measures for teacher preparation programs, in the form of two components, strategies and solutions of teacher training curricula, separated by curricula (formal, informal, informal, unstructured curriculum) and with the goal of preparing The formation of teachers has been identified in the educational system to realize the competencies of democratic social education. Conclusion: Therefore, examining the opportunities, facilities, and resources available to achieve these actions should be on the agenda of decision makers and educational planners. It is necessary to pay special attention to the strategies and solutions of teacher training curricula.

In this paper, a step-by-step laboratory procedure for performing a satellite's payload’s Alignment measurement is presented. Four highly accurate theodolites are used along with two or more Alignment corner cube to accurately extract the final attitude. Theodolites are arranged around the satellite in such a way that they have a clear direct view of the Alignment cubes mounted on the payload and the satellite. Two theodolites should point to the payload’s Alignment cube and the other two theodolites must point to the satellite’s Alignment cube. Each theodolite must see at least one other theodolite, directly. Finally, by forming the coordinates systems of the payload and satellite in the theodolites coordinate system along with using the coordinate transfer matrices, the payload Alignment correction matrix will be extracted in detail. The total method accuracy is within the order of few arcseconds.

One of the most important organizational challenges is the strategic Alignment between information technology (IT) and business objectives. Organizations have deployed different approaches to achieve strategic Alignment and competitive advantage. Enterprise architecture (EA) has been viewed as an effective approach not only for optimum management of IT, but also for strategic Alignment of IT applications and business needs. EA maturity indicates the degree of attaining EA project goals, out of which, is the IT-Business Alignment. In this paper, the relationship between enterprise architecture maturity (EAM) and strategic Alignment maturity model (SAMM) has been investigated to declare whether EAM can act as enabler for SAMM. Research population includes enterprises in which, the EA project is implemented. Pearson’s correlation and regression test have been used for analyzing the data gathered by questionnaire. The findings indicate that the success of EA significantly influences strategic Alignment maturity.

One of the critical stages in a display production line is the image Alignment of displays that includes the precise adjustment of the geometric parameters and the color of the image. The mutual influences of the parameters of the display's image necessitate a complex and interactive Alignment process. In this paper, the effect of the mutual influences of geometric parameters on the Alignment process of a display's image are shown, a suitable model for the geometric characteristics of a display's image are suggested and, then, the unknown parameters of the proposed model are estimated by the RLS estimator. Using an off-line estimator, an initial measure of the values of the unknown parameters of the display model is obtained. To modify the model parameters of the consecutive video displays on the production line, an on-line estimator is applied. Variations of the parameters of the display model on a production line are traced using on-line estimation. This model estimation is used to implement an adaptive Alignment algorithm. Both the adaptive and the proportional Alignment processes have been experimentally implemented under similar working conditions. Experimental results show that the use of the adaptive Alignment process considerably increases the speed and reliability of convergence of geometric parameters to their desired values. An IA-32, 3.4 GHz Pentium P4 processor has been used in this research. Considering the rapid developments in UDSM technology and the IA-64 architecture, the application of the proposed adaptive Alignment algorithm in an auto-Alignment system has the potential for real-time implementation in the near future.

In this paper, we study the applicability of probabilistic solutions for the Alignment of tertiary structure of proteins and discuss its difference with the deterministic algorithms. For this purpose, we introduce two Bayesian models and address a solution to add amino acid sequence and type (primary structure) to protein Alignment. Furthermore, we will study the parameter estimation with Markov Chain Monte Carlo sampling from the posterior distribution. Finally, in order to see the effectiveness of these methods in the protein Alignment, we have compared the parameter estimations in a real data set.

Machine learning is an application of artificial intelligence that is able to automatically learn and improve from experience without being explicitly programmed. The primary assumption for most of the machine learning algorithms is that the training set (source domain) and the test set (target domain) follow from the same probability distribution. However, in most of the real-world applications, this assumption is violated since the probability distribution of the source and target domains are different. This issue is known as domain shift. Therefore, transfer learning and domain adaptation generalize the model to face target data with different distribution. In this paper, we propose a domain adaptation method referred to as IMage Alignment via KErnelized feature learning (IMAKE) in order to preserve the general and geometric information of the source and target domains. IMAKE finds a common subspace across domains to reduce the distribution discrepancy between the source and the target domains. IMAKE adapts both the geometric and the general distributions, simultaneously. Moreover, IMAKE transfers the source and target domains into a shared low dimensional subspace in an unsupervised manner. Our proposed method minimizes the marginal and conditional probability distribution differences of the source and target data via maximum mean discrepancy and manifold Alignment for geometrical distribution adaptation. IMAKE maps the input data into a common latent subspace via manifold Alignment as a geometric matching method. Therefore, the samples with the same class labels are collected around their means, and samples with different class are separated, as well. Moreover, IMAKE maintains the source and target domain manifolds to preserve the original data position and domain structure. Also, the use of kernels and mapping data into Hilbert space provides more accurate separation between different classes and is suitable for data with complex and unbalanced structures. The proposed method has been evaluated using a variety of benchmark visual databases with 36 experiments. The results indicate the significant improvements of the proposed method performance against other machine learning and transfer learning approaches.